In conventional control units, fuzzy logic systems used as a control element are static systems, and as such they lack dynamic transfer properties. Accordingly they have the properties of nonlinear, static transfer elements.
To achieve dynamic control properties of a fuzzy logic control unit, it is known to combine static fuzzy logic systems with conventional, linear dynamic control elements. Such linear dynamic control elements have in particular an integrating, differentiating or proportional transfer characteristic arbitrary combinations thereof. For instance, such linear dynamic control elements are also known as I, D, P, PI, PD or PID control elements.
As shown in FIGS. 1 and 2, in a control unit RE′ it is known for linear dynamic control elements with an integrating, differentiating and/or proportional transfer characteristic either to precede a static fuzzy logic system control element FU, as shown in FIG. 1, or to follow such a control element FU, as shown in FIG. 2. In FIGS. 1 and 2, in each case as examples, a linear dynamic control element R1′ with a proportional transfer characteristic, a linear dynamic control element R2′ with an integrating transfer characteristic, and a linear dynamic control element R3′ with a differentiating transfer characteristic are shown. The known control units RE′ with fuzzy logic properties shown in FIGS. 1 and 2, because of the linear dynamic control elements R1′ through R3′, in particular have the dynamic properties of a so-called PID controller.
From Fuzzy-Control by Mario Koch and others, R. Oldenbourg Verlag GmbH, Munich, 1996, pages 29–32 and 249–266, it is known, for generating dynamic transfer properties, for a control unit with integrating and/or differentiating transfer properties to be preceded or followed by a fuzzy logic system.
The known control units RE′, shown as examples in FIGS. 1 and 2, as a rule have a guide variable w′, in particular for specifying a desired control value, and a feedback variable r′. The feedback variable r′ is subtracted in the control unit RE′ from the guide variable w′ and supplied as a controlled difference e′=w′−r′ to the control elements R1′ through R3′, or to the fuzzy logic system control element FU. The output variables thereof, that is, of the control elements R1′ through R3′ or of the fuzzy logic system control element FU, are combined in the control unit RE′ and serve as an output variable y′ of the control unit RE′, in particular for regulating a technical process from which the feedback variable r′ is fed back, in particular as a so-called actual control value, to the control unit RE′.
It is disadvantageous that the conventional control units with fuzzy properties are based on the combination of two different systems, mainly static fuzzy logic devices with linear dynamic control element systems. It is especially disadvantageous that the intended introduction of nonlinearities is not possible at all, or at least not without major effort and expense, since in particular this requires knowledge of performance graph regulation or other additional skills. Varying and modifying the controlled properties is thus very complicated or entirely unfeasible. Furthermore, this makes certain desired control unit properties in control technology, such as in particular a nonlinear, limited integration control characteristic, such as the so-called “anti-wind-up” control characteristic, unfeasible.
From International Patent Reference WO 96/31304 and from “Breakout Prediction for Continuous Casting by Fuzzy Mealy Automata”, by J. Adamy, Proceedings of the 3rd European Congress of Intelligent Techniques and Soft Computing EUFIT, Aachen, Aug. 29–31, 1995, pages 754–759, a dynamic fuzzy system known as a fuzzy automaton is known for early breakout prediction in continuous casting.
From U.S. Pat. No. 3,272,621, a method and an apparatus for controlling a process involving idle time is known. The method comprises an evaluation of input signals in such a way that process output responses are set in relation to known input information. The input evaluation criteria are represented in one or more of the integrator, proportional and differential process responses.
From German patent disclosure DE 44 20 800 A1, a fuzzy PID controller is known in which, to shorten the calculation time needed to ascertain the controlling variable, the control quantity is minimized to two rules by limiting the relationship functions uses; the fuzzifications are freely selectable as differential and defuzzification methods.